Abstract
Statistical Schema Induction can be applied on an RDF dataset to induce domain and range restrictions. We extend an existing approach that derives independent domain and range restrictions to derive coupled domain/range restrictions, which may be beneficial in the context of Natural Language Processing tasks such as Semantic Parsing and Entity Classification. We provide results from an experiment on the DBpedia graph. An evaluation shows that high precision can be achieved. Code and data are available at https://github.com/ag-sc/SchemaInduction.
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Notes
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The prefixes dbo and dbr refer to http://dbpedia.org/ontology/ and http://dbpedia.org/resource/, respectively.
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Note that the authors of [9] do not explicitly mention that they derive frequent maximal itemsets only. But since non-maximal itemsets, such as empty itemsets, are irrelevant, we assume they perform frequent maximal itemset mining.
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Acknowledgements
This work was supported by the Cluster of Excellence Cognitive Interaction Technology ’CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).
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Ell, B., Hakimov, S., Cimiano, P. (2017). Statistical Induction of Coupled Domain/Range Restrictions from RDF Knowledge Bases. In: van Erp, M., et al. Knowledge Graphs and Language Technology. ISWC 2016. Lecture Notes in Computer Science(), vol 10579. Springer, Cham. https://doi.org/10.1007/978-3-319-68723-0_3
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